{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88960039",
   "metadata": {},
   "source": [
    "**<font size =6>报告三 —— FshionMINST服装分类</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36aa4aa",
   "metadata": {},
   "source": [
    "**<font size=5>问题背景与要求：</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ddf73b",
   "metadata": {},
   "source": [
    "<font size=4>手写数字MINST几乎是机器学习领域的“Hello World”，实际上，MNIST数据集已经成为算法作者的必测的数据集之一。有人曾调侃道：\"如果一个算法在MNIST不work，那么它就根本没法用；而如果它在MNIST上work，它在其他数据上也可能不work\"。然而，随着机器学习的不断发展，手写数字MINST在大多数网络面前显得过于简单，很多算法在测试集上的性能已经达到 99.6%！因此，FashionMINST应运而生。<br><br>\n",
    "FashionMNIST 是一个替代 MNIST 手写数字集的图像数据集。 它是由 Zalando（一家德国的时尚科技公司）旗下的研究部门提供。其涵盖了来自 10 种类别的共 7 万个不同商品的正面图片。Fashion-MNIST 的目的是要成为 MNIST 数据集的一个直接替代品，如果之前写过手写数字MINST的机器学习程序，那么不需要修改任何的代码，只需要将手写数字MINST的数据集换成FashionMNIST的数据集即可使用。Fashion-MNIST 的图片大小，训练、测试样本数及类别数与经典 MNIST 完全相同。<br><br>\n",
    "本次报告的目的是构建卷积神经网络，通过对FashionMNIST数据集的学习，能够较好地识别出真实情况下的衣服类型，并且对分类错的衣服图片进行纠正，以提高模型的泛化能力。</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30cc4ef3",
   "metadata": {},
   "source": [
    "**<font size=5>数据处理与初步分析：</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a8b07e",
   "metadata": {},
   "source": [
    "<font size=4>首先将所有要用到的库添加进来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f459e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "from torch.nn import Linear, ReLU, CrossEntropyLoss, Sequential, Conv2d, MaxPool2d, Module, Softmax, BatchNorm2d, Dropout\n",
    "from torch.optim import Adam, SGD\n",
    "import torch.backends.cudnn as cudnn\n",
    "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
    "from collections import Counter\n",
    "from PIL import Image,ImageTk\n",
    "import cv2\n",
    "import seaborn as sns\n",
    "\n",
    "from pytorchtool import EarlyStopping# 关于 EarlyStopping 的代码可以看pytorchtool.py的程序"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06717f96",
   "metadata": {},
   "source": [
    "<font size=4>首先判断是否能使用cuda加速运算，如果可以则使用cuda，如果不可以则使用cpu进行运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4009b7ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda:0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76f29cd",
   "metadata": {},
   "source": [
    "<font size=4>将fashionMINST的数据集加载进来，分为训练集和测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "255ae1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=np.load('fashion-minst/t10k-images.npy')\n",
    "y_train=np.load('fashion-minst/t10k-labels.npy')\n",
    "x_test=np.load('fashion-minst/train-images.npy')\n",
    "y_test=np.load('fashion-minst/train-labels.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69174ee9",
   "metadata": {},
   "source": [
    "<font size=4>对训练集和测试集进行reshape，并对它们进行归一化，使数据分布在[-1,1]之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecfe34aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.reshape((60000,1,28,28))\n",
    "x_train=x_train/128-1\n",
    "x_test=x_test.reshape((10000,1,28,28))\n",
    "x_test=x_test/128-1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0466f5c1",
   "metadata": {},
   "source": [
    "<font size=4>在一开始进行测试时，发现训练好模型后，使用网络上找的图片进行分类时，若图片具有方向性，比如数据集中凉鞋均是朝向左侧，而找来的图片是朝向右侧，那么分类的结果就非常不理想，于是我将数据集中所有的图片进行镜像后再添加到数据集中，训练集的长度由60000扩充到120000，测试集的长度由10000扩充到20000，这样模型同时训练了原数据和镜像后的数据，也就能分辨出镜像后的图片了。</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656941bd",
   "metadata": {},
   "source": [
    "<font size=4>以下是对数据集进行镜像并添加到训练集和测试集中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "caf04211",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=np.vstack((x_train,x_train))\n",
    "for i in range(len(x_train)):\n",
    "    j=np.fliplr(x_train[i][0])\n",
    "    j=np.expand_dims(np.expand_dims(j, axis=0),axis=0)\n",
    "    temp[60000+i]=j\n",
    "x_train=temp\n",
    "y_train=np.hstack((y_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ca77ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=np.vstack((x_test,x_test))\n",
    "for i in range(len(x_test)):\n",
    "    j=np.fliplr(x_test[i][0])\n",
    "    j=np.expand_dims(np.expand_dims(j, axis=0),axis=0)\n",
    "    temp[10000+i]=j\n",
    "x_test=temp\n",
    "y_test=np.hstack((y_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83abb674",
   "metadata": {},
   "source": [
    "<font size=4>将训练集和测试集的数据类型转为tensor类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69fba238",
   "metadata": {},
   "outputs": [],
   "source": [
    "m=[]\n",
    "n=[]\n",
    "p=[]\n",
    "q=[]\n",
    "for i in range(len(x_train)):\n",
    "    m.append(x_train[i])\n",
    "    n.append(y_train[i])\n",
    "for i in range(len(x_test)):\n",
    "    p.append(x_test[i])\n",
    "    q.append(y_test[i])\n",
    "m=np.array(m)\n",
    "n=np.array(n)\n",
    "p=np.array(p)\n",
    "q=np.array(q)\n",
    "src_train=torch.tensor(m)\n",
    "trg_train=torch.tensor(n)\n",
    "src_test=torch.tensor(p)\n",
    "trg_test=torch.tensor(q)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13942b74",
   "metadata": {},
   "source": [
    "<font size=4>将训练集和测试集载入到DataLoader数据迭代器中，分批次进行学习，加快模型训练速度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "688fb2e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Batch_size=1250\n",
    "data_train = TensorDataset(src_train, trg_train)\n",
    "data_loader_train = DataLoader(data_train, batch_size=Batch_size, shuffle=False)\n",
    "\n",
    "data_test = TensorDataset(src_test, trg_test)\n",
    "data_loader_test = DataLoader(data_test, batch_size=Batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d018d1a",
   "metadata": {},
   "source": [
    "<font size=4>定义卷积神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6bf316d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(Module):   \n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.cnn_layers = Sequential(\n",
    "            # 定义2D卷积层\n",
    "            Conv2d(1, 8, kernel_size=3, stride=1, padding=1),\n",
    "            BatchNorm2d(8),\n",
    "            ReLU(inplace=True),\n",
    "            MaxPool2d(kernel_size=2, stride=2),\n",
    "            # 定义另一个2D卷积层\n",
    "            Conv2d(8, 64, kernel_size=3, stride=1, padding=1),\n",
    "            BatchNorm2d(64),\n",
    "            ReLU(inplace=True),\n",
    "            MaxPool2d(kernel_size=2, stride=2),\n",
    "        )\n",
    "        self.linear_layers = Sequential(\n",
    "            Linear(64 * 7 * 7, 8*7*7),\n",
    "            Dropout(p=0.5),\n",
    "            Linear(8 * 7 * 7, 11)\n",
    "        )\n",
    "    # 前项传播\n",
    "    def forward(self, x):\n",
    "        x = self.cnn_layers(x)\n",
    "        x = x.view(-1, 64 * 7 * 7)\n",
    "        x = self.linear_layers(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2091ef",
   "metadata": {},
   "source": [
    "<font size=4>初始化模型，定义优化器为Adam，损失函数为交叉熵函数，设置最大循环为100，若连续15个循环测试集的损失没有下降则提前结束。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d4b3ee3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (cnn_layers): Sequential(\n",
      "    (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (4): Conv2d(8, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (linear_layers): Sequential(\n",
      "    (0): Linear(in_features=3136, out_features=392, bias=True)\n",
      "    (1): Dropout(p=0.5, inplace=False)\n",
      "    (2): Linear(in_features=392, out_features=11, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 定义模型\n",
    "model = Net().to(device)\n",
    "# 定义优化器\n",
    "optimizer = Adam(model.parameters())\n",
    "# 定义loss函数\n",
    "lossF = CrossEntropyLoss()\n",
    "EPOCHS=100\n",
    "early_stopping = EarlyStopping(15, verbose=True)# 关于 EarlyStopping 的代码可以看pytorchtool.py的程序\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83493cab",
   "metadata": {},
   "source": [
    "<font size=4>听说加了以下代码后模型训练速度会变快，具体原因是“设置 torch.backends.cudnn.benchmark=True 将会让程序在开始时花费一点额外时间，为整个网络的每个卷积层搜索最适合它的卷积实现算法，进而实现网络的加速。”，实际操作中速度似乎是变快了那么一点点，就留着了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78d056fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "cudnn.deterministic = True\n",
    "cudnn.benchmark = True\n",
    "torch.backends.cudnn.enabled = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7eb29c2",
   "metadata": {},
   "source": [
    "<font size=4>开始训练模型，定义trainloss_list和testloss_list用来存储每一个循环后的训练集和测试集的损失Loss，在训练结束后画图进行查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "865ed6fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[1/100] Loss: 0.4264, Acc: 0.8400: 100%|█████████████████████████████████████████████| 96/96 [00:09<00:00, 10.52step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (inf --> 0.920843).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2/100] Loss: 0.3493, Acc: 0.8752: 100%|█████████████████████████████████████████████| 96/96 [00:07<00:00, 13.54step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.920843 --> 0.406965).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[3/100] Loss: 0.3068, Acc: 0.8912: 100%|█████████████████████████████████████████████| 96/96 [00:07<00:00, 13.55step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.406965 --> 0.350989).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[4/100] Loss: 0.3147, Acc: 0.8832: 100%|█████████████████████████████████████████████| 96/96 [00:07<00:00, 13.54step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.350989 --> 0.333598).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[5/100] Loss: 0.2687, Acc: 0.9048: 100%|█████████████████████████████████████████████| 96/96 [00:07<00:00, 13.53step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.333598 --> 0.312962).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[6/100] Loss: 0.2690, Acc: 0.9008: 100%|█████████████████████████████████████████████| 96/96 [00:07<00:00, 13.55step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.312962 --> 0.308083).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[7/100] Loss: 0.2684, Acc: 0.9032: 100%|█████████████████████████████████████████████| 96/96 [00:07<00:00, 13.67step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.308083 --> 0.291820).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[8/100] Loss: 0.2555, Acc: 0.9040: 100%|█████████████████████████████████████████████| 96/96 [00:07<00:00, 13.56step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.291820 --> 0.291391).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[9/100] Loss: 0.2497, Acc: 0.9136: 100%|█████████████████████████████████████████████| 96/96 [00:07<00:00, 13.56step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.291391 --> 0.288684).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[10/100] Loss: 0.2504, Acc: 0.9080: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.51step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.288684 --> 0.288439).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[11/100] Loss: 0.2469, Acc: 0.9104: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.57step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.288439 --> 0.281332).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[12/100] Loss: 0.2410, Acc: 0.9120: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.67step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss decreased (0.281332 --> 0.274920).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[13/100] Loss: 0.2280, Acc: 0.9152: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.54step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 1 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[14/100] Loss: 0.2185, Acc: 0.9200: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.67step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 2 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[15/100] Loss: 0.2130, Acc: 0.9272: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.54step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 3 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[16/100] Loss: 0.2183, Acc: 0.9288: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.36step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 4 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[17/100] Loss: 0.1923, Acc: 0.9320: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.53step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 5 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[18/100] Loss: 0.1990, Acc: 0.9320: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.55step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 6 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[19/100] Loss: 0.1893, Acc: 0.9352: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.28step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 7 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[20/100] Loss: 0.1878, Acc: 0.9296: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.52step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 8 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[21/100] Loss: 0.1919, Acc: 0.9304: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.55step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 9 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[22/100] Loss: 0.1870, Acc: 0.9424: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.52step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 10 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[23/100] Loss: 0.1806, Acc: 0.9408: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.46step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 11 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[24/100] Loss: 0.1759, Acc: 0.9432: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.55step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 12 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[25/100] Loss: 0.1796, Acc: 0.9376: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.37step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 13 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[26/100] Loss: 0.1734, Acc: 0.9416: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.43step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 14 out of 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[27/100] Loss: 0.1830, Acc: 0.9352: 100%|████████████████████████████████████████████| 96/96 [00:07<00:00, 13.65step/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarlyStopping counter: 15 out of 15\n",
      "Early stopping\n"
     ]
    }
   ],
   "source": [
    "trainloss_list=[]\n",
    "testloss_list=[]\n",
    "\n",
    "for epoch in range(1,1+EPOCHS):  \n",
    "    model.train()#神经网络训练模式\n",
    "    #开始对训练集的DataLoader进行迭代\n",
    "    processBar = tqdm(data_loader_train,unit = 'step')#设置进度条实现训练可视化，迭代对象为训练集的ataLoader\n",
    "    for step,(trainImgs,labels) in enumerate(processBar):\n",
    "        #将图像和标签传输进device中\n",
    "        trainImgs = trainImgs.float().to(device)\n",
    "        labels = labels.to(device)\n",
    "        #清空模型的梯度\n",
    "        model.zero_grad()\n",
    "        #对模型进行前向推理\n",
    "        outputs = model(trainImgs)\n",
    "        #计算本轮推理的Loss值\n",
    "        loss = lossF(outputs,labels)\n",
    "        #计算本轮推理的准确率\n",
    "        predictions = torch.argmax(outputs, dim = 1)\n",
    "        accuracy = torch.sum(predictions == labels)/labels.shape[0]\n",
    "        #进行反向传播求出模型参数的梯度\n",
    "        loss.backward()\n",
    "        #使用迭代器更新模型权重\n",
    "        optimizer.step()\n",
    "        #将本step结果进行可视化处理\n",
    "        processBar.set_description(\"[%d/%d] Loss: %.4f, Acc: %.4f\" % \n",
    "                                       (epoch,EPOCHS,loss.item(),accuracy))\n",
    "        \n",
    "    pred_train=[]\n",
    "    correct = 0\n",
    "    model.eval()\n",
    "    for trainImgs,labels in data_loader_train:\n",
    "        trainImgs = trainImgs.float().to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(trainImgs)\n",
    "        pred_train.extend(outputs.cpu().tolist())\n",
    "    train_output=torch.Tensor(pred_train)\n",
    "    train_loss = lossF(train_output, trg_train.long())# 注意这里的输入参数维度要符合要求，我这里为了简单，并未考虑这一点\n",
    "    trainloss_list.append(train_loss.tolist())\n",
    "    \n",
    "    pred_test=[]\n",
    "    correct = 0\n",
    "    for testImgs,labels in data_loader_test:\n",
    "        testImgs = testImgs.float().to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(testImgs)\n",
    "        pred_test.extend(outputs.cpu().tolist())\n",
    "    test_output=torch.Tensor(pred_test)\n",
    "    test_loss = lossF(test_output, trg_test.long())# 注意这里的输入参数维度要符合要求，我这里为了简单，并未考虑这一点\n",
    "    testloss_list.append(test_loss.tolist())\n",
    "    \n",
    "    early_stopping(test_loss, model)\n",
    "    if early_stopping.early_stop:\n",
    "        print(\"Early stopping\")\n",
    "        # 结束模型训练\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fd8932",
   "metadata": {},
   "source": [
    "<font size=4>画出每一个循环后训练集和测试集的损失Loss变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de485166",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,1+epoch),trainloss_list,label='Train Loss')\n",
    "plt.plot(range(1,1+epoch),testloss_list,label='Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b500861c",
   "metadata": {},
   "source": [
    "<font size=4>可以看到在第12个循环后测试集的损失Loss几乎不变，在经过15个循环后损失Loss没有下降，因此便提前停止学习防止过拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad08d4a3",
   "metadata": {},
   "source": [
    "<font size=4>检验测试集的准确度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b34f4e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testAccuracy: 0.9004\n"
     ]
    }
   ],
   "source": [
    "pred_test=[]\n",
    "model.eval()\n",
    "for testImgs,labels in data_loader_test:\n",
    "    testImgs = testImgs.float().to(device)\n",
    "    labels = labels.to(device)\n",
    "    outputs = model(testImgs)\n",
    "    predictions = torch.argmax(outputs.cpu(),dim = 1).tolist()\n",
    "    pred_test.extend(predictions)\n",
    "testAccuracy=accuracy_score(q.tolist(),pred_test)\n",
    "\n",
    "print('testAccuracy:',testAccuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4694d127",
   "metadata": {},
   "source": [
    "<font size=4>可以看到准确度达到了90%左右，效果不错，接下来输出混淆矩阵看看是那些类容易分错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "548e4b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C=confusion_matrix(q.tolist(),pred_test) # 可将'1'等替换成自己的类别，如'cat'。\n",
    "x=range(0,10)\n",
    "y=range(0,10)\n",
    "sns.heatmap(C,fmt='g',annot=True,cbar=False,xticklabels=x, yticklabels=y) #画热力图,annot=True 代表 在图上显示 对应的值， fmt 属性 代表输出值的格式，cbar=False, 不显示 热力棒\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9752b4",
   "metadata": {},
   "source": [
    "<font size=4>接下来我在网上找了50张图片，每类分别为5张，用来测试模型对真实图片的分类是否准确"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "281c03d7",
   "metadata": {},
   "source": [
    "<font size=4>形成50张图片的路径，并根据图片顺序生成标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7e19c7a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "str2=['t-shirts','trousers','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle boot']\n",
    "str1=[]\n",
    "for j in str2:\n",
    "    for i in range(5):\n",
    "        str3='Image\\\\'+j+str(i+1)+'.jfif'\n",
    "        str1.append(str3)\n",
    "label=[0,0,0,0,0,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5,6,6,6,6,6,7,7,7,7,7,8,8,8,8,8,9,9,9,9,9]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca39d14c",
   "metadata": {},
   "source": [
    "<font size=4>将图片载入进来，并将图片进行压缩成28×28分辨率、灰度反转并归一化，与数据集保持一致。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "199ed7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "t=np.array([])\n",
    "for i in str1:\n",
    "    photo = cv2.imread(i, cv2.IMREAD_GRAYSCALE)#读取图片灰度图\n",
    "    resized = cv2.resize(photo, (28,28), interpolation = cv2.INTER_AREA)#将图片压缩到28*28的分辨率，与模型输入保持一致\n",
    "    resized=255-resized#将灰度反转，即白底黑字转换为黑底白字，与训练集保持一致\n",
    "    resized=resized/128-1#归一化\n",
    "    resized=resized.astype(np.float32) #转换为32位浮点数格式，与训练集保持一致\n",
    "    t=np.append(t,np.array([[resized]]))\n",
    "t=t.reshape((int(len(t)/28/28),1,28,28))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c50beb1",
   "metadata": {},
   "source": [
    "<font size=4>生成50张图片的预览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "276a0b5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 12000x4000 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=500,figsize=(24,8))\n",
    "for i in range(1,51):\n",
    "    plt.subplot(10,5,i)\n",
    "    plt.imshow(t[i-1][0],cmap='gray')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e6be85",
   "metadata": {},
   "source": [
    "<font size=4>对这50张图片进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9b42210f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testAccuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "t1=torch.tensor(t)#转换为tensor格式\n",
    "t1=t1.float().to(device)#将图像传输进device中\n",
    "model.eval()#神经网络预测模式\n",
    "out=model(t1)#用神经网络预测结果\n",
    "print('testAccuracy:',accuracy_score(torch.argmax(out.cpu(),dim = 1).tolist(),label))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f646d5a5",
   "metadata": {},
   "source": [
    "<font size=4>可以看到准确度达到了80%，效果还是不错的。输出混淆矩阵看看是哪些类容易分错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bfe9b9cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C=confusion_matrix(label,torch.argmax(out.cpu(),dim = 1).tolist()) # 可将'1'等替换成自己的类别，如'cat'。\n",
    "x=range(0,10)\n",
    "y=range(0,10)\n",
    "sns.heatmap(C,fmt='g',annot=True,cbar=False,xticklabels=x, yticklabels=y) #画热力图,annot=True 代表 在图上显示 对应的值， fmt 属性 代表输出值的格式，cbar=False, 不显示 热力棒\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cede3dc",
   "metadata": {},
   "source": [
    "<font size=4>可以看到混淆矩阵的分布与测试集的混淆矩阵比较类似，因此可以证明训练出来的模型泛化能力还是不错的。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55f6e0f",
   "metadata": {},
   "source": [
    "<font size=4>接下来针对被分错的图片，我考虑通过手动输入正确的类型来帮助模型学习，具体是在原有模型的基础上，进入训练模式，将上传的图片作为新训练集传入模型继续学习，并进入预测模式预测，再学习，再预测，直到模型预测上传图片正确为止，既避免训练次数较少导致拟合度不够，预测依然错误，又避免过拟合，导致泛化效果低。如此反复操作，最后保存模型，可以得到能较好识别实际情况下的fashion-MINST卷积神经网络模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "141a596f",
   "metadata": {},
   "source": [
    "<font size=4>为此我写了以下程序，并引入tkinter模块，将功能用GUI图形界面的形式展示出来，可以方便地操作，具体操作过程如下：\n",
    "运行以下代码后，会出现一个“打开图片”按钮，按下此按钮后会让用户选择一张图片。在正确选择图片后，会显示图片的预览，并出现“预测类型”按钮，按下按钮后，经过短暂运算，会给出对该图片衣服类型做出的预测，如果类别正确，可以按下“继续预测”按钮进行新一轮的预测，如果结果错误，则可以在新出现的文本框内输入正确的类型，然后按下“确定按钮”，程序会将此图片添加到训练集中继续学习，当出现“训练完成”字样时，可以按下“继续预测”按钮进行新一轮的预测。按下“保存模型”按钮，会让用户选择一个路径来保存当前的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a6635f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "from tkinter import *\n",
    "from tkinter import ttk\n",
    "from tkinter.filedialog import *\n",
    "import threading\n",
    "\n",
    "#参数初始化，为后续训练做准备\n",
    "t3=np.array([])\n",
    "t3=torch.FloatTensor(t3)\n",
    "in4=np.array([])\n",
    "in4=torch.IntTensor(in4)\n",
    " \n",
    "class GUI():\n",
    "    def __init__(self, root):\n",
    "        self.initGUI(root)\n",
    "    def initGUI(self, root):#GUI初始化，将所有需要用到的控件添加进来\n",
    "        root.title(\"fashionMINST预测\")#界面标题\n",
    "        root.geometry('500x300')#界面初始尺寸\n",
    "\n",
    "        #Label标签类控件\n",
    "        self.label1 = Label(root, text='请打开想要识别的图片', fg='black', font=('Arial', 12), width=30, height=2)\n",
    "        self.label2 = Label(root, text='正在训练模型', fg='black', font=('Arial', 12), width=30, height=2)\n",
    "        self.label3 = Label(root, text='', fg='black', font=('Arial', 12), width=30, height=2)\n",
    "        self.label4 = Label(root, text='请输入正确类型，帮助模型学习\\n或选择再次预测', fg='black', font=('Arial', 12), width=40, height=2)\n",
    "        self.label5 = Label(root, text='正在训练模型', fg='black', font=('Arial', 12), width=30, height=2)\n",
    "        self.image_label = Label(root,width=300, height=300)\n",
    "        #Button按钮类控件\n",
    "        self.button_1 = Button(root, text=\"打开图片\", width=10, command=self.A)\n",
    "        self.button_2 = Button(root, text=\"预测类型\", width=10, command=self.B)\n",
    "        self.button_3 = Button(root, text=\"确定\", width=10, command=self.C)\n",
    "        self.button_4 = Button(root, text=\"再次预测\", width=10, command=self.D)\n",
    "        self.button_5 = Button(root, text=\"保存模型\", width=10, command=self.E)\n",
    "        #Entry文本输入类控件\n",
    "        self.entry1=Entry(root,show=None,font=('Arial', 14))\n",
    "        self.entry2=Entry(root,show=None,font=('Arial', 14))\n",
    "        #摆放控件\n",
    "        self.label1.pack()\n",
    "        self.button_1.pack()\n",
    "        self.button_5.place(x=400,y=20,anchor='nw')#放置‘保存模型’按钮\n",
    "        #进入消息循环\n",
    "        root.mainloop()\n",
    "\n",
    "    #按下‘打开图片’按钮\n",
    "    def A(self):\n",
    "        global resized\n",
    "        filepath = askopenfilename(filetypes=[('ALL','.*'),('JPG','.jpg'),('PNG','png'),('JFIF','.jfif')])  # 选择打开什么图片\n",
    "        img = Image.open(filepath)  # 打开图片\n",
    "        img = img.resize((300, 300),Image.ANTIALIAS)\n",
    "        img = ImageTk.PhotoImage(img)  # 用PIL模块的PhotoImage打开\n",
    "        \n",
    "        self.image_label = Label(root)\n",
    "        self.image_label.config(image=img,width=300, height=300)\n",
    "        self.image_label.image = img\n",
    "        self.image_label.pack()#放置图片label，预览图片\n",
    "        \n",
    "        photo = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)#读取图片灰度图\n",
    "        resized = cv2.resize(photo, (28,28), interpolation = cv2.INTER_AREA)#将图片压缩到28*28的分辨率，与模型输入保持一致\n",
    "        resized=255-resized#将灰度反转，即白底黑字转换为黑底白字，与训练集保持一致\n",
    "        resized=resized/128-1#归一化\n",
    "        resized=resized.astype(np.float32) #转换为32位浮点数格式，与训练集保持一致\n",
    "        \n",
    "        self.button_2 = Button(root, text=\"预测类型\", width=10, command=self.B)\n",
    "        self.button_2.pack()\n",
    "\n",
    "    #按下‘预测类型’按钮\n",
    "    def B(self):\n",
    "        global model,resized,t1\n",
    "        t=np.array([[resized]])#升维，与训练集保持一致\n",
    "        t1=torch.tensor(t)#转换为tensor格式\n",
    "        t1=t1.to(device)#将图像传输进device中\n",
    "        model.eval()#神经网络预测模式\n",
    "        out=model(t1)#用神经网络预测结果\n",
    "        pred=str(torch.argmax(out.cpu(),dim = 1).numpy())\n",
    "        \n",
    "        self.label2 = Label(root, text='预测结果为：', fg='black', font=('Arial', 12), width=30, height=2)\n",
    "        self.label2.pack()\n",
    "        self.label3 = Label(root, text=pred, fg='black', font=('Arial', 12), width=30, height=2)\n",
    "        self.label3.pack()#显示预测结果\n",
    "        self.label4 = Label(root, text='请输入正确类型，帮助模型学习\\n或选择再次预测', fg='black', font=('Arial', 12), width=40, height=2)\n",
    "        self.label4.pack()\n",
    "        self.entry1=Entry(root,show=None,font=('Arial', 14))\n",
    "        self.entry1.pack()#放置文本输入框用来输入正确数字\n",
    "        self.button_3 = Button(root, text=\"确定\", width=10, command=self.C)\n",
    "        self.button_3.pack()\n",
    "        self.button_4 = Button(root, text=\"再次预测\", width=10, command=self.D)\n",
    "        self.button_4.pack()\n",
    "\n",
    "    #按下‘确定’按钮，开始再次训练\n",
    "    def C(self):\n",
    "        global model,t1,in4,t3,in2     \n",
    "        in2=self.entry1.get()#获取文本框输入数字\n",
    "        if in2=='':#输入为空，提醒再次输入\n",
    "            self.label4['text']='输入为空，请重新输入'\n",
    "            root.update()\n",
    "        else:\n",
    "            self.label5 = Label(root, text='正在训练模型，预计需要10s', fg='black', font=('Arial', 12), width=30, height=2)\n",
    "            self.label5.pack()\n",
    "            in2=int(in2)#将str转为int\n",
    "            t3=torch.cat((t3.to(device),t1.to(device)),0)#将此图片加入到新训练集中\n",
    "            t3=t3.to(device)#将图像传输进device中\n",
    "            in3=torch.tensor([int(in2)], dtype=int)#将图片正确结果转换为tensor格式\n",
    "            in4=torch.cat((in4.to(device),in3.to(device)),0)#将图片正确结果加入到新标签中\n",
    "            in4=in4.to(device)#将图像传输进device中\n",
    "            \n",
    "            T = threading.Thread(target=self.__C)#为避免训练时GUI卡死，将训练模型的过程放到子线程中\n",
    "            T.start()#开始线程\n",
    "    \n",
    "    def __C(self):\n",
    "        global model,t1,in4,t3,in2\n",
    "        learning_rate = 1e-2#定义初始学习率\n",
    "        lossF = nn.CrossEntropyLoss()#定义损失函数为交叉熵损失函数\n",
    "        optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)#定义优化器为Adam\n",
    "        while True:\n",
    "            model.train()#神经网络训练模式\n",
    "            #开始对训练集的DataLoader进行迭代\n",
    "            for step,(trainImgs,labels) in enumerate(data_loader_train):\n",
    "                #将图像和标签传输进device中\n",
    "                trainImgs = trainImgs.float().to(device)\n",
    "                labels = labels.to(device)\n",
    "                #清空模型的梯度\n",
    "                model.zero_grad()\n",
    "                #对模型进行前向推理\n",
    "                outputs = model(trainImgs)#训练集的预测结果\n",
    "                output=model(t3)#新训练集的预测结果\n",
    "                outputs=torch.cat((outputs,output),0)#将二者合并起来与标签进行比对\n",
    "                labels=torch.cat((labels,in4),0)#将训练集与新训练集的标签合并起来\n",
    "                #计算本轮推理的Loss值\n",
    "                loss = lossF(outputs,labels)\n",
    "                #计算本轮推理的准确率\n",
    "                predictions = torch.argmax(outputs, dim = 1)\n",
    "                accuracy = torch.sum(predictions == labels)/labels.shape[0]\n",
    "                #进行反向传播求出模型参数的梯度\n",
    "                loss.backward()\n",
    "                #使用迭代器更新模型权重\n",
    "                optimizer.step()\n",
    "            model.eval()#神经网络预测模式\n",
    "            out=model(t1)#预测输入图片的标签\n",
    "            if torch.argmax(out.cpu(),dim = 1).numpy()[0]==int(in2):#如果预测正确则退出再学习\n",
    "                break\n",
    "        self.label5['text']='训练完成，可以再次预测'\n",
    "        root.update()\n",
    "        \n",
    "        \n",
    "    #按下‘再次预测’按钮\n",
    "    def D(self):\n",
    "        self.label1.pack_forget()\n",
    "        self.label2.pack_forget()\n",
    "        self.label3.pack_forget()\n",
    "        self.label4.pack_forget()\n",
    "        self.label5.pack_forget()\n",
    "        self.image_label.pack_forget()\n",
    "        self.button_1.pack_forget()\n",
    "        self.button_2.pack_forget()\n",
    "        self.button_3.pack_forget()\n",
    "        self.button_4.pack_forget()\n",
    "        self.entry1.pack_forget()#将之前的控件全部隐藏，避免控件越叠越多\n",
    "\n",
    "        self.button_5.place(x=400,y=20,anchor='nw')#放置‘保存模型’按钮\n",
    "        self.label1 = Label(root, text='请打开想要识别的图片', fg='black', font=('Arial', 12), width=30, height=2)\n",
    "        self.label1.pack()\n",
    "        self.button_1 = Button(root, text=\"打开图片\", width=10, command=self.A)\n",
    "        self.button_1.pack()\n",
    "\n",
    "    #按下‘保存模型’按钮\n",
    "    def E(self):\n",
    "        filenewpath = asksaveasfilename(defaultextension='.pth')#设置保存文件，指定文件名后缀为.pth\n",
    "        torch.save(model,filenewpath )#保存模型到指定目录\n",
    "root = Tk()\n",
    "myGUI = GUI(root)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fed94319",
   "metadata": {},
   "source": [
    "**<font size=5>总结</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d2f9db",
   "metadata": {},
   "source": [
    "<font size=4>fashion-MINST问题是一个多分类问题，在解决这一问题的过程中，我学会了将数据集用DataLoader封装起来，分批次进行训练和测试，这样能加快训练速度并减少内存的开销；学会了对图片进行各种操作，包括压缩分辨率、转为灰度图等操作；使用pytorch的Module类搭建卷积神经网络，对图片进行学习和分类，能够获得比一般神经网络更好的效果；利用卷积神经网络对实际情况下的衣服进行分类，使得机器学习更加贴近实际，更能发挥实际作用。此外，我还学习使用了tkinter模块构建GUI界面，让用户操作变得十分方便，还利用新的训练集对卷积神经网络模型进行再学习，能让神经网络变得更加强大。"
   ]
  }
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